The existing Raman microscopy technology is time-consuming, difficult to use, expensive, and not available to most scientists, engineers, medical doctors and technical personnel who need to test optical/chemical properties of the sample at the nanoscale—in most cases simultaneously with conventional AFM imaging.
Attempts at combining AFM and optical techniques have been made in the past. Combining AFM with traditional microscopy methods has become quite common, as it provides a means for simultaneous application of external forces, quantitative measurements and direct visualization of the perturbed surface of the sample. Much less evolved is the combination of AFM and Raman spectroscopy.
Conventional Raman spectroscopy is known to suffer from low efficiency and sensitivity, as it relies on the highly inefficient process of inelastic light scattering. Surface-enhanced Raman spectroscopy (SERS) combines spontaneous Raman spectroscopy with the local field enhancement capability of gold or silver nanostructures to amplify the signal by 3 to 4 orders of magnitude. Using the same field-enhancing effects as in SERS, tip-enhanced Raman spectroscopy (TERS) combines the strong enhancement of SERS and scanning probe imaging to achieve high spatial resolution (˜30 nm) with high sensitivity. TERS is a near-field technique, where a metallic or metallized scanning probe microscopy (SPM) tip is used as an optical nano-antenna to confine and enhance an electromagnetic field in close proximity to the sample surface. In TERS, the external excitation laser light is used for side illumination of the tip. TERS performance, therefore, is challenged by low efficiency of coupling the laser light into the near-field mode of the optical AFM probe, leading to a tremendous optical power loss and large background signal. TERS would greatly benefit from further sensitivity enhancement, but this cannot be achieved by simply increasing the excitation power. New excitation/detection strategies are required to solve the TERS low signal-to-noise ratio (data quality) problem.